In [110]:
import pandas as pd
import numpy as np
import matplotlib as plt
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
In [72]:
df_train=pd.read_csv('tested.csv')# import the csv file
df_train.head(100)# read the csv file
Out[72]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 0 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 1 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 0 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 0 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 1 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
95 987 0 3 Tenglin, Mr. Gunnar Isidor male 25.0 0 0 350033 7.7958 NaN S
96 988 1 1 Cavendish, Mrs. Tyrell William (Julia Florence... female 76.0 1 0 19877 78.8500 C46 S
97 989 0 3 Makinen, Mr. Kalle Edvard male 29.0 0 0 STON/O 2. 3101268 7.9250 NaN S
98 990 1 3 Braf, Miss. Elin Ester Maria female 20.0 0 0 347471 7.8542 NaN S
99 991 0 3 Nancarrow, Mr. William Henry male 33.0 0 0 A./5. 3338 8.0500 NaN S

100 rows × 12 columns

In [73]:
df_train.describe()# used to the aggreagte value of numerical column
Out[73]:
PassengerId Survived Pclass Age SibSp Parch Fare
count 418.000000 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000
mean 1100.500000 0.363636 2.265550 30.272590 0.447368 0.392344 35.627188
std 120.810458 0.481622 0.841838 14.181209 0.896760 0.981429 55.907576
min 892.000000 0.000000 1.000000 0.170000 0.000000 0.000000 0.000000
25% 996.250000 0.000000 1.000000 21.000000 0.000000 0.000000 7.895800
50% 1100.500000 0.000000 3.000000 27.000000 0.000000 0.000000 14.454200
75% 1204.750000 1.000000 3.000000 39.000000 1.000000 0.000000 31.500000
max 1309.000000 1.000000 3.000000 76.000000 8.000000 9.000000 512.329200
In [74]:
df_train.shape # used to identify the rows and columns in the dataframe
Out[74]:
(418, 12)
In [75]:
for i in df_train.columns:# this code use to get the column name
    print(i)

PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
In [76]:
# Data Cleaning
In [77]:
missed_vales=df_train.isnull().sum()#identify the misisng values in dataset
missed_vales
Out[77]:
PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age             86
SibSp            0
Parch            0
Ticket           0
Fare             1
Cabin          327
Embarked         0
dtype: int64
In [78]:
df_train["Cabin"].value_counts()# Value_counts() used for the number occurence of the record in the particular column
Out[78]:
Cabin
B57 B59 B63 B66    3
B45                2
C89                2
C55 C57            2
A34                2
                  ..
E52                1
D30                1
E31                1
C62 C64            1
C105               1
Name: count, Length: 76, dtype: int64
In [79]:
len(df_train)
Out[79]:
418
In [80]:
missed_vales/len(df_train) * 100 # percentage  of missing values
Out[80]:
PassengerId     0.000000
Survived        0.000000
Pclass          0.000000
Name            0.000000
Sex             0.000000
Age            20.574163
SibSp           0.000000
Parch           0.000000
Ticket          0.000000
Fare            0.239234
Cabin          78.229665
Embarked        0.000000
dtype: float64
In [81]:
df_train.dropna(subset = ['Fare'], inplace=True)# drop the missing values record in the Fare column
In [82]:
df_train['Cabin']=df_train['Cabin'].replace(to_replace= np.nan,value="Unknown") # replace the missing values with unknown
In [83]:
df_train.isnull().sum()
Out[83]:
PassengerId     0
Survived        0
Pclass          0
Name            0
Sex             0
Age            86
SibSp           0
Parch           0
Ticket          0
Fare            0
Cabin           0
Embarked        0
dtype: int64
In [84]:
df_train['Age'] = df_train['Age'].fillna(df_train['Age'].mode()[0]) # treat the missing values with mode.
In [85]:
df_train.isnull().sum()
Out[85]:
PassengerId    0
Survived       0
Pclass         0
Name           0
Sex            0
Age            0
SibSp          0
Parch          0
Ticket         0
Fare           0
Cabin          0
Embarked       0
dtype: int64
In [86]:
# Outliers are those data points that are significantly different from the rest of the dataset. here i am using the boxplot.
In [87]:
sns.boxplot(df_train['PassengerId']) # there is no outlier in the chart
Out[87]:
<Axes: >
In [88]:
sns.boxplot(df_train['Survived'])
Out[88]:
<Axes: >
In [89]:
sns.boxplot(df_train['Pclass'])
Out[89]:
<Axes: >
In [90]:
sns.boxplot(df_train['Age'])
Out[90]:
<Axes: >
In [91]:
print(np.where(df_train['Age']>55)) # remove the outliers

(array([  2,  13,  48,  69,  81,  96, 114, 142, 178, 192, 212, 216, 235,
       304, 315, 342, 355, 386], dtype=int64),)
In [92]:
df_train['Sex'].value_counts()
Out[92]:
Sex
male      265
female    152
Name: count, dtype: int64
In [94]:
df_train['Sex']=df_train['Sex'].replace('male',1) # here male replace with 1 and femlae replace with 2
In [54]:
df_train['Sex'].replace('female',2)
Out[54]:
0      1
1      2
2      1
3      1
4      2
      ..
413    1
414    2
415    1
416    1
417    1
Name: Sex, Length: 418, dtype: int64
In [55]:
sns.boxplot(df_train['Sex'])
Out[55]:
<Axes: >
In [56]:
sns.boxplot(df_train['SibSp'])
Out[56]:
<Axes: >
In [57]:
sns.boxplot(df_train['Parch'])
Out[57]:
<Axes: >
In [58]:
sns.boxplot(df_train['Fare'])
Out[58]:
<Axes: >
In [59]:
#Univariate anlaysis
In [60]:
#A scatter plot is a diagram where each value in the data set is represented by a dot.
In [61]:
plt.scatter(df_train.index,df_train['PassengerId'])
plt.show()
In [62]:
plt.scatter(df_train.index,df_train['Survived'])
Out[62]:
<matplotlib.collections.PathCollection at 0x281e86eeb50>
In [63]:
plt.scatter(df_train.index,df_train['Pclass'])
Out[63]:
<matplotlib.collections.PathCollection at 0x281e8733d10>
In [64]:
plt.scatter(df_train.index,df_train['Sex'])
Out[64]:
<matplotlib.collections.PathCollection at 0x281e88da110>
In [65]:
plt.scatter(df_train.index,df_train['Age'])
Out[65]:
<matplotlib.collections.PathCollection at 0x281e85d6b50>
In [66]:
plt.scatter(df_train.index,df_train['SibSp'])
Out[66]:
<matplotlib.collections.PathCollection at 0x281e8396590>
In [67]:
plt.scatter(df_train.index,df_train['Ticket'] )
Out[67]:
<matplotlib.collections.PathCollection at 0x281e761ff50>
In [68]:
plt.scatter(df_train.index,df_train['Fare'])
Out[68]:
<matplotlib.collections.PathCollection at 0x281e874cb90>
In [95]:
plt.scatter(df_train.index,df_train['Cabin'])
Out[95]:
<matplotlib.collections.PathCollection at 0x281ed12f090>
In [96]:
plt.scatter(df_train.index,df_train['Embarked'])
Out[96]:
<matplotlib.collections.PathCollection at 0x281ed1f40d0>
In [ ]:
#stripplot
In [97]:
sns.stripplot(y=df_train['PassengerId'])
Out[97]:
<Axes: ylabel='PassengerId'>
In [98]:
sns.stripplot(y=df_train['Survived'])
Out[98]:
<Axes: ylabel='Survived'>
In [99]:
sns.stripplot(y=df_train['Pclass'])
Out[99]:
<Axes: ylabel='Pclass'>
In [100]:
sns.stripplot(y=df_train['SibSp'])
Out[100]:
<Axes: ylabel='SibSp'>
In [101]:
sns.stripplot(y=df_train['Age'])
Out[101]:
<Axes: ylabel='Age'>
In [102]:
sns.stripplot(y=df_train['Parch'])
Out[102]:
<Axes: ylabel='Parch'>
In [103]:
sns.stripplot(y=df_train['Ticket'])
Out[103]:
<Axes: ylabel='Ticket'>
In [104]:
sns.stripplot(y=df_train['Cabin'])
Out[104]:
<Axes: ylabel='Cabin'>
In [105]:
sns.stripplot(y=df_train['Fare'])
Out[105]:
<Axes: ylabel='Fare'>
In [106]:
sns.stripplot(y=df_train['Embarked'])
Out[106]:
<Axes: ylabel='Embarked'>
In [112]:
#A histogram is a graph showing frequency distributions. It is a graph showing the number of observations within each given interval.
In [113]:
plt.hist(df_train['PassengerId'])
Out[113]:
(array([42., 42., 42., 40., 42., 42., 41., 42., 42., 42.]),
 array([ 892. ,  933.7,  975.4, 1017.1, 1058.8, 1100.5, 1142.2, 1183.9,
        1225.6, 1267.3, 1309. ]),
 <BarContainer object of 10 artists>)
In [114]:
plt.hist(df_train['Survived'])
Out[114]:
(array([265.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 152.]),
 array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),
 <BarContainer object of 10 artists>)
In [115]:
plt.hist(df_train['Pclass'])
Out[115]:
(array([107.,   0.,   0.,   0.,   0.,  93.,   0.,   0.,   0., 217.]),
 array([1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]),
 <BarContainer object of 10 artists>)
In [116]:
plt.hist(df_train['SibSp'])
Out[116]:
(array([282., 110.,  14.,   4.,   0.,   4.,   1.,   0.,   0.,   2.]),
 array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ]),
 <BarContainer object of 10 artists>)
In [117]:
plt.hist(df_train['Parch'])
Out[117]:
(array([323.,  52.,  33.,   3.,   2.,   1.,   1.,   0.,   0.,   2.]),
 array([0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),
 <BarContainer object of 10 artists>)
In [118]:
plt.hist(df_train['Ticket'])
Out[118]:
(array([49., 42., 43., 40., 51., 42., 38., 38., 37., 37.]),
 array([  0. ,  36.1,  72.2, 108.3, 144.4, 180.5, 216.6, 252.7, 288.8,
        324.9, 361. ]),
 <BarContainer object of 10 artists>)
In [119]:
plt.hist(df_train['Fare'])
Out[119]:
(array([338.,  48.,  11.,   2.,  10.,   7.,   0.,   0.,   0.,   1.]),
 array([  0.     ,  51.23292, 102.46584, 153.69876, 204.93168, 256.1646 ,
        307.39752, 358.63044, 409.86336, 461.09628, 512.3292 ]),
 <BarContainer object of 10 artists>)
In [120]:
plt.hist(df_train['Cabin'])
Out[120]:
(array([337.,  10.,   9.,  10.,   8.,  10.,  10.,   7.,   8.,   8.]),
 array([ 0. ,  7.6, 15.2, 22.8, 30.4, 38. , 45.6, 53.2, 60.8, 68.4, 76. ]),
 <BarContainer object of 10 artists>)
In [121]:
plt.hist(df_train['Embarked'])
Out[121]:
(array([ 46.,   0.,   0.,   0.,   0., 269.,   0.,   0.,   0., 102.]),
 array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),
 <BarContainer object of 10 artists>)
In [122]:
#Bivariate analysis
In [123]:
#Bivariate analysis is one of the statistical analysis where two variables are observed. One variable here is dependent while the other is independent.
In [124]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Survived'])
Out[124]:
<Axes: ylabel='PassengerId'>
In [125]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Pclass'])
Out[125]:
<Axes: ylabel='PassengerId'>
In [126]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Age'])
Out[126]:
<Axes: ylabel='PassengerId'>
In [127]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['SibSp'])
Out[127]:
<Axes: ylabel='PassengerId'>
In [128]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Parch'])
Out[128]:
<Axes: ylabel='PassengerId'>
In [129]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Ticket'])
Out[129]:
<Axes: ylabel='PassengerId'>
In [130]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Fare'])
Out[130]:
<Axes: ylabel='PassengerId'>
In [131]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Cabin'])
Out[131]:
<Axes: ylabel='PassengerId'>
In [132]:
sns.scatterplot(x=df_train.index,y=df_train['PassengerId'],hue=df_train['Embarked'])
Out[132]:
<Axes: ylabel='PassengerId'>
In [133]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['Pclass'])
Out[133]:
<Axes: ylabel='Survived'>
In [134]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['PassengerId'])
Out[134]:
<Axes: ylabel='Survived'>
In [135]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['SibSp'])
Out[135]:
<Axes: ylabel='Survived'>
In [136]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['Parch'])
Out[136]:
<Axes: ylabel='Survived'>
In [137]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['Ticket'])
Out[137]:
<Axes: ylabel='Survived'>
In [138]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['Fare'])
Out[138]:
<Axes: ylabel='Survived'>
In [139]:
sns.scatterplot(x=df_train.index,y=df_train['Survived'],hue=df_train['Embarked'])
Out[139]:
<Axes: ylabel='Survived'>
In [140]:
sns.scatterplot(x=df_train.index,y=df_train['Pclass'],hue=df_train['PassengerId'])
Out[140]:
<Axes: ylabel='Pclass'>
In [141]:
sns.scatterplot(x=df_train.index,y=df_train['Pclass'],hue=df_train['Survived'])
Out[141]:
<Axes: ylabel='Pclass'>
In [142]:
sns.scatterplot(x=df_train.index,y=df_train['Pclass'],hue=df_train['SibSp'])
Out[142]:
<Axes: ylabel='Pclass'>
In [143]:
sns.scatterplot(x=df_train.index,y=df_train['Pclass'],hue=df_train['Parch'])
Out[143]:
<Axes: ylabel='Pclass'>
In [144]:
sns.scatterplot(x=df_train.index,y=df_train['Pclass'],hue=df_train['Ticket'])
Out[144]:
<Axes: ylabel='Pclass'>
In [145]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['PassengerId'])
Out[145]:
<Axes: ylabel='SibSp'>
In [146]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Survived'])
Out[146]:
<Axes: ylabel='SibSp'>
In [147]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Pclass'])
Out[147]:
<Axes: ylabel='SibSp'>
In [148]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Parch'])
Out[148]:
<Axes: ylabel='SibSp'>
In [149]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Ticket'])
Out[149]:
<Axes: ylabel='SibSp'>
In [150]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Fare'])
Out[150]:
<Axes: ylabel='SibSp'>
In [151]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Cabin'])
Out[151]:
<Axes: ylabel='SibSp'>
In [154]:
sns.scatterplot(x=df_train.index,y=df_train['SibSp'],hue=df_train['Embarked'])
Out[154]:
<Axes: ylabel='SibSp'>
In [155]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Survived'])
Out[155]:
<Axes: xlabel='PassengerId', ylabel='Survived'>
In [156]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Pclass'])
Out[156]:
<Axes: xlabel='PassengerId', ylabel='Pclass'>
In [157]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['SibSp'])
Out[157]:
<Axes: xlabel='PassengerId', ylabel='SibSp'>
In [158]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Parch'])
Out[158]:
<Axes: xlabel='PassengerId', ylabel='Parch'>
In [159]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Ticket'])
Out[159]:
<Axes: xlabel='PassengerId', ylabel='Ticket'>
In [160]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Fare'])
Out[160]:
<Axes: xlabel='PassengerId', ylabel='Fare'>
In [161]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Cabin'])
Out[161]:
<Axes: xlabel='PassengerId', ylabel='Cabin'>
In [162]:
sns.stripplot(x=df_train['PassengerId'],y=df_train['Embarked'])
Out[162]:
<Axes: xlabel='PassengerId', ylabel='Embarked'>
In [163]:
sns.stripplot(x=df_train['Survived'],y=df_train['PassengerId'])
Out[163]:
<Axes: xlabel='Survived', ylabel='PassengerId'>
In [164]:
sns.stripplot(x=df_train['Survived'],y=df_train['Pclass'])
Out[164]:
<Axes: xlabel='Survived', ylabel='Pclass'>
In [165]:
sns.stripplot(x=df_train['Survived'],y=df_train['SibSp'])
Out[165]:
<Axes: xlabel='Survived', ylabel='SibSp'>
In [166]:
sns.stripplot(x=df_train['Survived'],y=df_train['Parch'])
Out[166]:
<Axes: xlabel='Survived', ylabel='Parch'>
In [167]:
sns.stripplot(x=df_train['Survived'],y=df_train['Ticket'])
Out[167]:
<Axes: xlabel='Survived', ylabel='Ticket'>
In [168]:
sns.stripplot(x=df_train['Survived'],y=df_train['Fare'])
Out[168]:
<Axes: xlabel='Survived', ylabel='Fare'>
In [169]:
sns.stripplot(x=df_train['Survived'],y=df_train['Cabin'])
Out[169]:
<Axes: xlabel='Survived', ylabel='Cabin'>
In [170]:
sns.stripplot(x=df_train['Survived'],y=df_train['Embarked'])
Out[170]:
<Axes: xlabel='Survived', ylabel='Embarked'>
In [171]:
sns.stripplot(x=df_train['Pclass'],y=df_train['PassengerId'])
Out[171]:
<Axes: xlabel='Pclass', ylabel='PassengerId'>
In [172]:
sns.stripplot(x=df_train['Pclass'],y=df_train['Survived'])
Out[172]:
<Axes: xlabel='Pclass', ylabel='Survived'>
In [173]:
sns.stripplot(x=df_train['Pclass'],y=df_train['SibSp'])
Out[173]:
<Axes: xlabel='Pclass', ylabel='SibSp'>
In [174]:
sns.stripplot(x=df_train['Pclass'],y=df_train['Parch'])
Out[174]:
<Axes: xlabel='Pclass', ylabel='Parch'>
In [175]:
sns.stripplot(x=df_train['Pclass'],y=df_train['Ticket'])
Out[175]:
<Axes: xlabel='Pclass', ylabel='Ticket'>
In [176]:
sns.stripplot(x=df_train['Pclass'],y=df_train['Fare'])
Out[176]:
<Axes: xlabel='Pclass', ylabel='Fare'>
In [177]:
sns.stripplot(x=df_train['Pclass'],y=df_train['Cabin'])
Out[177]:
<Axes: xlabel='Pclass', ylabel='Cabin'>
In [178]:
sns.stripplot(x=df_train['Pclass'],y=df_train['Embarked'])
Out[178]:
<Axes: xlabel='Pclass', ylabel='Embarked'>
In [179]:
sns.stripplot(x=df_train['Age'],y=df_train['PassengerId'])
Out[179]:
<Axes: xlabel='Age', ylabel='PassengerId'>
In [180]:
sns.stripplot(x=df_train['Age'],y=df_train['Survived'])
Out[180]:
<Axes: xlabel='Age', ylabel='Survived'>
In [181]:
sns.stripplot(x=df_train['Age'],y=df_train['Pclass'])
Out[181]:
<Axes: xlabel='Age', ylabel='Pclass'>
In [182]:
sns.stripplot(x=df_train['Age'],y=df_train['Ticket'])
Out[182]:
<Axes: xlabel='Age', ylabel='Ticket'>
In [183]:
sns.stripplot(x=df_train['Age'],y=df_train['Cabin'])
Out[183]:
<Axes: xlabel='Age', ylabel='Cabin'>
In [184]:
sns.stripplot(x=df_train['Age'],y=df_train['Cabin'])
Out[184]:
<Axes: xlabel='Age', ylabel='Cabin'>
In [185]:
sns.stripplot(x=df_train['Age'],y=df_train['Embarked'])
Out[185]:
<Axes: xlabel='Age', ylabel='Embarked'>
In [186]:
#Correlation
In [187]:
df_2 = df_train.drop(['Name', 'Ticket','Cabin','PassengerId'], axis=1) # drop the name ,ticket,cabin,passengerid columns
In [188]:
df_2
Out[188]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 1 34.5 0 0 7.8292 Q
1 1 3 2 47.0 1 0 7.0000 S
2 0 2 1 62.0 0 0 9.6875 Q
3 0 3 1 27.0 0 0 8.6625 S
4 1 3 2 22.0 1 1 12.2875 S
... ... ... ... ... ... ... ... ...
413 0 3 1 21.0 0 0 8.0500 S
414 1 1 2 39.0 0 0 108.9000 C
415 0 3 1 38.5 0 0 7.2500 S
416 0 3 1 21.0 0 0 8.0500 S
417 0 3 1 21.0 1 1 22.3583 C

417 rows × 8 columns

In [191]:
df_2['Embarked']=df_2['Embarked'].replace("S",1)
In [192]:
df_2['Embarked']=df_2['Embarked'].replace("Q",2)
In [193]:
df_2['Embarked']=df_2['Embarked'].replace("C",3)
In [194]:
df_2['Embarked'].value_counts()
Out[194]:
Embarked
1    269
3    102
2     46
Name: count, dtype: int64
In [195]:
#data preprocessing
In [196]:
#correlation The corr() method calculates the relationship between each column in your data set.
In [198]:
sns.heatmap(df_2.corr(),cmap="YlGnBu") # In this Heatmap , sex and survived have high relationship compare to rest of the column
Out[198]:
<Axes: >
In [199]:
#feature Scaling
In [200]:
#Data standardization is converting data into a standard format that computers can easily understand and use.
In [201]:
# Create a StandardScaler object
scaler = StandardScaler()

# Fit the scaler to your data
scaler.fit(df_2)

# Perform standardization on the DataFrame
df_standardized = scaler.transform(df_2)
# Convert the standardized array back to a DataFrame
df_standardized = pd.DataFrame(df_standardized, columns=df_2.columns)
df_standardized
Out[201]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 -0.757354 0.875330 -0.757354 0.474738 -0.500218 -0.400804 -0.497811 0.468949
1 1.320387 0.875330 1.320387 1.429986 0.615242 -0.400804 -0.512660 -0.702020
2 -0.757354 -0.313636 -0.757354 2.576283 -0.500218 -0.400804 -0.464532 0.468949
3 -0.757354 0.875330 -0.757354 -0.098411 -0.500218 -0.400804 -0.482888 -0.702020
4 1.320387 0.875330 1.320387 -0.480510 0.615242 0.618314 -0.417971 -0.702020
... ... ... ... ... ... ... ... ...
412 -0.757354 0.875330 -0.757354 -0.556930 -0.500218 -0.400804 -0.493856 -0.702020
413 1.320387 -1.502602 1.320387 0.818627 -0.500218 -0.400804 1.312180 1.639919
414 -0.757354 0.875330 -0.757354 0.780417 -0.500218 -0.400804 -0.508183 -0.702020
415 -0.757354 0.875330 -0.757354 -0.556930 -0.500218 -0.400804 -0.493856 -0.702020
416 -0.757354 0.875330 -0.757354 -0.556930 0.615242 0.618314 -0.237621 1.639919

417 rows × 8 columns

In [203]:
#normalization
In [202]:
# Create a MinMaxScaler object
scaler = MinMaxScaler()

# Fit the scaler to your data
scaler.fit(df_2)

# Perform normalization on the DataFrame
df_normalized = scaler.transform(df_2)

# Convert the normalized array back to a DataFrame
df_normalized = pd.DataFrame(df_normalized, columns=df_2.columns)

# Print the normalized DataFrame
df_normalized
Out[202]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0.0 1.0 0.0 0.452723 0.000 0.000000 0.015282 0.5
1 1.0 1.0 1.0 0.617566 0.125 0.000000 0.013663 0.0
2 0.0 0.5 0.0 0.815377 0.000 0.000000 0.018909 0.5
3 0.0 1.0 0.0 0.353818 0.000 0.000000 0.016908 0.0
4 1.0 1.0 1.0 0.287881 0.125 0.111111 0.023984 0.0
... ... ... ... ... ... ... ... ...
412 0.0 1.0 0.0 0.274693 0.000 0.000000 0.015713 0.0
413 1.0 0.0 1.0 0.512066 0.000 0.000000 0.212559 1.0
414 0.0 1.0 0.0 0.505473 0.000 0.000000 0.014151 0.0
415 0.0 1.0 0.0 0.274693 0.000 0.000000 0.015713 0.0
416 0.0 1.0 0.0 0.274693 0.125 0.111111 0.043640 1.0

417 rows × 8 columns

In [ ]: